Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …

The increased complexity and intelligence of automation systems require the development
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …

In real industries, there often exist application scenarios where the target domain holds fault
categories never observed in the source domain, which is an open-set domain adaptation …

As an indispensable energy device, 18650 lithium-ion battery has widespread applications
in electric vehicles. Remaining useful life (RUL) prediction of lithium-ion battery is critical for …

Component reliability plays a pivotal role in industrial systems, which are evolving with
larger complexity and higher dimensionality of data. It is insufficient to ensure reliability and …

Many deep-learning methods have been developed for fault diagnosis. However, due to the
difficulty of collecting and labeling machine fault data, the datasets in some practical …

Fault-tolerant control (FTC) may conceal fault symptoms, thereby increasing the difficulty of
fault diagnosis (FD). In this article, a novel framework for the cooperative design of active FD …

As the “heart” of high-speed train, traction systems play an important role in the safe
operation of trains, of which the operation and maintenance level is still unable to meet the …

The lack of grasp of the image information and the unstable fluctuation of the model energy
may cause segmentation failure of the active contour model (ACM). Minimizing the impact of …

A well-known shortcoming of the traditional canonical correlation analysis (CCA) is the lack
of robustness against outliers. This shortcoming hinders the application of CCA in the case …